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Biotechnological advances in sequencing have led to an explosion of publicly available data via large international consortia such as The Cancer Genome Atlas (TCGA), The Encyclopedia of DNA Elements (ENCODE), and The NIH Roadmap Epigenomics Mapping Consortium (Roadmap). These projects have provided unprecedented opportunities to interrogate the epigenome of cultured cancer cell lines as well as normal and tumor tissues with high genomic resolution. The Bioconductor project offers more than 1,000 open-source software and statistical packages to analyze high-throughput genomic data. However, most packages are designed for specific data types (e.g. expression, epigenetics, genomics) and there is no one comprehensive tool that provides a complete integrative analysis of the resources and data provided by all three public projects. A need to create an integration of these different analyses was recently proposed. In this workflow, we provide a series of biologically focused integrative analyses of different molecular data. We describe how to download, process and prepare TCGA data and by harnessing several key Bioconductor packages, we describe how to extract biologically meaningful genomic and epigenomic data. Using Roadmap and ENCODE data, we provide a work plan to identify biologically relevant functional epigenomic elements associated with cancer. To illustrate our workflow, we analyzed two types of brain tumors: low-grade glioma (LGG) versus high-grade glioma (glioblastoma multiform or GBM). This workflow introduces the following Bioconductor packages: AnnotationHub, ChIPSeeker, ComplexHeatmap, pathview, ELMER, GAIA, MINET, RTCGAToolbox,  TCGAbiolinks.

Strawberry (Fragaria × ananassa) fruits contain high concentrations of flavonoids. In unripe strawberries, the flavonoids are mainly represented by proanthocyanidins (PAs), while in ripe fruits the red-coloured anthocyanins also accumulate. Most of the structural genes leading to PA biosynthesis in strawberry have been characterized, but no information is available on their transcriptional regulation. In Arabidopsis thaliana the expression of the PA biosynthetic genes is specifically induced by a ternary protein complex, composed of AtTT2 (AtMYB123), AtTT8 (AtbHLH042) and AtTTG1 (WD40-repeat protein). A strategy combining yeast-two-hybrid screening and agglomerative hierarchical clustering of transcriptomic and metabolomic data was undertaken to identify strawberry PA regulators. Among the candidate genes isolated, four were similar to AtTT2, AtTT8 and AtTTG1 (FaMYB9/FaMYB11, FabHLH3 and FaTTG1, respectively) and two encode putative negative regulators (FaMYB5 and FabHLH3Δ). Interestingly, FaMYB9/FaMYB11, FabHLH3 and FaTTG1 were found to complement the tt2-1, tt8-3 and ttg1-1 transparent testa mutants, respectively. In addition, they interacted in yeast and activated the Arabidopsis BANYULS (anthocyanidin reductase) gene promoter when coexpressed in Physcomitrella patens protoplasts. Taken together, these results demonstrated that FaMYB9/FaMYB11, FabHLH3 and FaTTG1 are the respective functional homologues of AtTT2, AtTT8 and AtTTG1, providing new tools for modifying PA content and strawberry fruit quality.

Root identity in plant embryos During Arabidopsis embryogenesis, a single cell — called the hypophysis — is specified to become the founder cell of the root meristem in response to signals from adjacent cells. Hypophysis specification requires an auxin responsive transcription factor, MONOPTEROS, which promotes transport of auxin from the embryo to the hypophysis precursor. In this study, Dolf Weijers and colleagues identify MONOPTEROS target genes and show how they mediate root formation. During Arabidopsis embryogenesis, a single cell is specified to become the founder cell of the root meristem — the hypophysis — in response to signals from adjacent cells. Hypophysis specification requires an auxin-responsive transcription factor, MONOPTEROS (MP), which promotes transport of auxin from the embryo to the hypophysis precursor. Here, MP target genes are identified and the means by which they mediate root formation is shown. Acquisition of cell identity in plants relies strongly on positional information 1 , hence cell–cell communication and inductive signalling are instrumental for developmental patterning. During Arabidopsis embryogenesis, an extra-embryonic cell is specified to become the founder cell of the primary root meristem, hypophysis, in response to signals from adjacent embryonic cells 2 . The auxin-dependent transcription factor MONOPTEROS (MP) drives hypophysis specification by promoting transport of the hormone auxin from the embryo to the hypophysis precursor. However, auxin accumulation is not sufficient for hypophysis specification, indicating that additional MP-dependent signals are required 3 . Here we describe the microarray-based isolation of MP target genes that mediate signalling from embryo to hypophysis. Of three direct transcriptional target genes, TARGET OF MP 5 ( TMO5 ) and TMO7 encode basic helix–loop–helix (bHLH) transcription factors that are expressed in the hypophysis-adjacent embryo cells, and are required and partially sufficient for MP-dependent root initiation. Importantly, the small TMO7 transcription factor moves from its site of synthesis in the embryo to the hypophysis precursor, thus representing a novel MP-dependent intercellular signal in embryonic root specification.

In order to improve the complexity of the chaotic system and ensure the relevant security indicators of the cryptographic algorithm, a new chaotic image encryption algorithm based on dynamic DNA coding and RNA computing is proposed. In this paper, we first construct a four-dimensional hyperchaotic system with more complex dynamics and then use the plaintext-related keystream generated by the hyperchaotic system to dynamically DNA encode the plaintext image, then perform RNA coding conversion and amino acid substitution box generation, and finally use an improved replacement sequence generator to generate pseudo-random sequences for replacement operations to generate the final ciphertext image. Theoretical analysis and simulation results show that the proposed algorithm has excellent performance in security indicators such as key space, the number of pixels change rate, the number average changing intensity, entropy, clipping attack, noise attack, and chosen plaintext attack. Therefore, the algorithm has higher security.

Brain-computer interface (BCI) can be used as a real-time bidirectional information gateway between the brain and machines. In particular, rapid progress in invasive BCI, propelled by recent developments in electrode materials, miniature and power-efficient electronics, and neural signal decoding technologies has attracted wide attention. In this review, we first introduce the concepts of neuronal signal decoding and encoding that are fundamental for information exchanges in BCI. Then, we review the history and recent advances in invasive BCI, particularly through studies using neural signals for controlling external devices on one hand, and modulating brain activity on the other hand. Specifically, regarding modulating brain activity, we focus on two types of techniques, applying electrical stimulation to cortical and deep brain tissues, respectively. Finally, we discuss the related ethical issues concerning the clinical application of this emerging technology.

Because of their simple design structure, end-to-end deep learning (E2E-DL) models have gained a lot of attention for speech enhancement. A number of DL models have achieved excellent results in eliminating the background noise and enhancing the quality as well as the intelligibility of noisy speech. Designing resource-efficient and compact models during real-time processing is still a key challenge. In order to enhance the accomplishment of E2E models, the sequential and local characteristics of speech signal should be efficiently taken into consideration while modeling. In this paper, we present resource-efficient and compact neural models for end-to-end noise-robust waveform-based speech enhancement. Combining the Convolutional Encode-Decoder (CED) and Recurrent Neural Networks (RNNs) in the Convolutional Recurrent Network (CRN) framework, we have aimed at different speech enhancement systems. Different noise types and speakers are used to train and test the proposed models. With LibriSpeech and the DEMAND dataset, the experiments show that the proposed models lead to improved quality and intelligibility with fewer trainable parameters, notably reduced model complexity, and inference time than existing recurrent and convolutional models. The quality and intelligibility are improved by 31.61% and 17.18% over the noisy speech. We further performed cross corpus analysis to demonstrate the generalization of the proposed E2E SE models across different speech datasets.

Transposable elements (TEs) represent a substantial fraction of many eukaryotic genomes, and transcriptional regulation of these factors is important to determine TE activities in human cells. However, due to the repetitive nature of TEs, identifying transcription factor (TF)-binding sites from ChIP-sequencing (ChIP-seq) datasets is challenging. Current algorithms are focused on subtle differences between TE copies and thus bias the analysis to relatively old and inactive TEs. Here we describe an approach termed “MapRRCon” (mapping repeat reads to a consensus) which allows us to identify proteins binding to TE DNA sequences by mapping ChIP-seq reads to the TE consensus sequence after whole-genome alignment. Although this method does not assign binding sites to individual insertions in the genome, it provides a landscape of interacting TFs by capturing factors that bind to TEs under various conditions. We applied this method to screen TFs’ interaction with L1 in human cells/tissues using ENCODE ChIP-seq datasets and identified 178 of the 512 TFs tested as bound to L1 in at least one biological condition with most of them (138) localized to the promoter. Among these L1-binding factors, we focused on Myc and CTCF, as they play important roles in cancer progression and 3D chromatin structure formation. Furthermore, we explored the transcriptomes of The Cancer Genome Atlas breast and ovarian tumor samples in which a consistent anti-/correlation between L1 and Myc/CTCF expression was observed, suggesting that these two factors may play roles in regulating L1 transcription during the development of such tumors.

Resistances to begomoviruses, including bipartite tomato mottle virus and monopartite tomato yellow leaf curl virus (TYLCV), have been introgressed to cultivated tomato ( Solanum lycopersicum ) from wild tomato accessions. A major gene, Ty - 2 from S. habrochaites f. glabratum accession “B6013 ,” that confers resistance to TYLCV was previously mapped to a 19-cM region on the long arm of chromosome 11. In the present study, approximately 11,000 plants were screened and nearly 157 recombination events were identified between the flanking markers C2_At1g07960 (82.5 cM, physical distance 51.387 Mb) and T0302 (89 cM, 51.878 Mb). Molecular marker analysis of recombinants and TYLCV evaluation of progeny from these recombinants localized Ty - 2 to an approximately 300,000-bp interval between markers UP8 (51.344 Mb) and M1 (51.645 Mb). No recombinants were identified between TG36 and C2_At3g52090, a region of at least 115 kb, indicating severe recombination suppression in this region. Due to the small interval, fluorescence in situ hybridization analysis failed to clarify whether recombination suppression is caused by chromosomal rearrangements. Candidate genes predicted based on tomato genome annotation were analyzed by RT-PCR and virus-induced gene silencing. Results indicate that the NBS gene family present in the Ty - 2 region is likely not responsible for the Ty - 2 -conferred resistance and that two candidate genes might play a role in the Ty - 2 -conferred resistance. Several markers very tightly linked to the Ty - 2 locus are presented and useful for marker-assisted selection in breeding programs to introgress Ty - 2 for begomovirus resistance.

The ability of metal hyperaccumulating plants to tolerate and accumulate heavy metals results from adaptations of metal homeostasis. NRAMP metal transporters were found to be highly expressed in some hyperaccumulating plant species. Here, we identified TcNRAMP3 and TcNRAMP4, the closest homologues to AtNRAMP3 and AtNRAMP4 in Thlaspi caerulescens and characterized them by expression analysis, confocal imaging and heterologous expression in yeast and Arabidopsis thaliana. TcNRAMP3 and TcNRAMP4 are expressed at higher levels than their A. thaliana homologues. When expressed in yeast TcNRAMP3 and TcNRAMP4 transport the same metals as their respective A. thaliana orthologues: iron (Fe), manganese (Mn) and cadmium (Cd) but not zinc (Zn) for NRAMP3; Fe, Mn, Cd and Zn for NRAMP4. They also localize at the vacuolar membrane in A. thaliana protoplasts. Inactivation of AtNRAMP3 and AtNRAMP4 in A. thaliana results in strong Cd and Zn hypersensitivity, which is fully rescued by TcNRAMP3 or TcNRAMP4 expression. However, metal tolerance conferred by TcNRAMP expression in nramp3nramp4 mutant does not exceed that of wild-type A. thaliana. Our data indicate that the difference between TcNRAMP3 and TcNRAMP4 and their A. thaliana orthologues does not lie in a different protein function, but probably resides in a different expression level or expression pattern.

Memristor has been widely studied in the fields of non-volatile memory, digital logic circuits and neuromorphic computing due to its ultra-high storage density and information processing capacity.. The current research on memristor crossbar array is mainly based on binary memristors and thus suited for binary logic. The crossbar array based on tri-valued memristors is proposed, along with its reset and read/write operations. We present a method for implementing a tri-valued memristor with two binary memristors. Tri-valued-memristor-based crossbar arrays significantly increase the storage density and the information processing capacity. An encode-store-decode circuit for three binary signals is designed using the crossbar array. It can transform three streams of binary signals to a single ternary signal by encoding and reverse transform the encoded signal by decoding. The designed circuits are verified by LTSpice simulation using the Knowm memristor model.